Walking speed and dual task input modality impact performance on a self-paced treadmill.

Interference between a walking task (target speeds on a self-paced treadmill) and dual visual and tactile-visual response time task was investigated. Ambulatory dual-task scenarios reveal how attention is divided between walking and additional tasks, but the impact of walking speed and dual-task modality on gait characteristics and dual-task performance is unclear. The purpose of this study was to evaluate the effect of visual and tactile-visual dual-task on gait performance. Participants (n=15) targeted four speeds (0.5, 1.0, 1.3, and 1.5 m/s) on a self-paced treadmill with a visual speed indicator (a green region centered at the target speed). Participants completed the same speed profile on the treadmill without (Self-Paced) and with a response time dual task (Self-Paced with Dual Task) requiring finger-tap responses to go/no-go cues. Six gait characteristics were calculated: proportion of time in the desired speed green region (GTP), speed ratio (ratio of mean to target speed), time to green region after target speed change (NRT), normalized stride width (NSW), normalized stride length (NSL), and stride time (ST). Both stride length and width were normalized by participant leg length. Lower GTP and greater speed ratio at slower speeds during dual tasking indicate speed-dependent changes in gait characteristics. Changes in NSL and ST were more affected by speed than dual task. These findings support that when speed is a parameter that is tracked, participants do not universally decrease speed in the presence of a dual task. These findings can support the decisions made when designing new wearable technologies that support navigation, communication, and mobility.

Alternative cue and response modalities maintain the Simon effect but impact task performance.

Inhibitory control, the ability to inhibit impulsive responses and irrelevant stimuli, enables high level functioning and activities of daily living. The Simon task probes inhibition using interfering stimuli, i.e., cues spatially presented on the opposite side of the indicated response; incongruent response times (RT) are slower than congruent RTs. Operational applicability of the Simon task beyond finger/hand manipulations and visual/auditory cues is unclear, but important to consider as new technologies provide tactile cues and require motor responses from the lower extremity (e.g., exoskeletons). In this study, twenty participants completed the Simon task under four conditions, each combination of two cue (visual/tactile) and response (upper/lower-extremity) modalities. RT were significantly longer for incongruent than congruent cues across cue-response pairs. However, alternative cue-response pairs yielded slower RT and decreased accuracy for tactile cues and lower-extremity responses. Results support operational usage of the Simon task to probe inhibition using tactile cues and lower-extremity responses relevant for new technologies like exoskeletons and immersive environments.

Utility of Inter-subject Transfer Learning for Wearable-Sensor-Based Joint Torque Prediction Models.

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.

A Neural Network Estimation of Ankle Torques From Electromyography and Accelerometry.

Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque estimates and sequences of torque predictions from motion capture and ground reaction forces to wearable sensor data using several modern types of neural networks. We use dense feedforward, convolutional, neural ordinary differential equation, and long short-term memory neural networks to learn the mapping for ankle plantarflexion and dorsiflexion torque during standing, walking, running, and sprinting, and consider both single-point torque estimation, as well as the prediction of a sequence of future torques. Our results show that long short-term memory neural networks, which consider incoming data sequentially, outperform dense feedforward, neural ordinary differential equation networks, and convolutional neural networks. Predictions of future ankle torques up to 0.4 s ahead also showed strong positive correlations with the actual torques. The proposed method relies on learning from a motion capture dataset, but once the model is built, the method uses wearable sensors that enable torque estimation without the motion capture data.

Event-Related Potentials, Inhibition, and Risk for Alzheimer's Disease Among Cognitively Intact Elders.

BACKGROUND: Despite advances in understanding Alzheimer's disease (AD), prediction of AD prior to symptom onset remains severely limited, even when primary risk factors such as the apolipoprotein E (APOE) ɛ4 allele are known. OBJECTIVE: Although executive dysfunction is highly prevalent and is a primary contributor to loss of independence in those with AD, few studies have examined neural differences underlying executive functioning as indicators of risk for AD prior to symptom onset, when intervention might be effective. METHODS: This study examined event-related potential (ERP) differences during inhibitory control in 44 cognitively intact older adults (20 ɛ4+, 24 ɛ4-), relative to 41 young adults. All participants completed go/no-go and stop-signal tasks. RESULTS: Overall, both older adult groups exhibited slower reaction times and longer ERP latencies compared to young adults. Older adults also had generally smaller N200 and P300 amplitudes, except at frontal electrodes and for N200 stop-signal amplitudes, which were larger in older adults. Considered with intact task accuracy, these findings suggest age-related neural compensation. Although ɛ4 did not distinguish elders during go or no-go tasks, this study uniquely showed that the more demanding stop-signal task was sensitive to ɛ4 differences, despite comparable task and neuropsychological performance with non-carriers. Specifically, ɛ4+ elders had slower frontal N200 latency and larger N200 amplitude, which was most robust at frontal sites, compared with ɛ4-. CONCLUSION: N200 during a stop-signal task is sensitive to AD risk, prior to any evidence of cognitive dysfunction, suggesting that stop-signal ERPs may be an important protocol addition to neuropsychological testing.

Sensorimotor conflict tests in an immersive virtual environment reveal subclinical impairments in mild traumatic brain injury.

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally. The results show that, as compared to standard clinical tests, the provocative perturbations illuminate balance impairments in subjects who have had mild traumatic brain injuries. Perturbations delivered while subjects were walking provided greater discriminability (average accuracy ≈ 0.90) than those delivered during standing (average accuracy ≈ 0.65) between mTBI subjects and healthy controls. Of the categories of features extracted to characterize balance, the lower limb accelerometry-based metrics proved to be most informative. Further, in response to perturbations, subjects with an mTBI utilized hip strategies more than ankle strategies to prevent loss of balance and also showed less variability in gait patterns. We have shown that sensorimotor conflicts illuminate otherwise-hidden balance impairments, which can be used to increase the sensitivity of current clinical procedures. This augmentation is vital in order to robustly detect the presence of balance impairments after mTBI and potentially define a phenotype of balance dysfunction that enhances risk of injury.

Measuring the plasticity of social approach: a randomized controlled trial of the effects of the PEERS intervention on EEG asymmetry in adolescents with autism spectrum disorders.

This study examined whether the Program for the Education and Enrichment of Relational Skills (PEERS: Social skills for teenagers with developmental and autism spectrum disorders: The PEERS treatment manual, Routledge, New York, 2010a) affected neural function, via EEG asymmetry, in a randomized controlled trial of adolescents with Autism spectrum disorders (ASD) and a group of typically developing adolescents. Adolescents with ASD in PEERS shifted from right-hemisphere gamma-band EEG asymmetry before PEERS to left-hemisphere EEG asymmetry after PEERS, versus a waitlist ASD group. Left-hemisphere EEG asymmetry was associated with more social contacts and knowledge, and fewer symptoms of autism. Adolescents with ASD in PEERS no longer differed from typically developing adolescents in left-dominant EEG asymmetry at post-test. These findings are discussed via the Modifier Model of Autism (Mundy et al. in Res Pract Persons Severe Disabl 32(2):124, 2007), with emphasis on remediating isolation/withdrawal in ASD.